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<em>Ecology & Bioinformatics</em>
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<em>Ecology & Bioinformatics</em> (ISSN 2072-6643) is a <a href="https://www.mdpi.com/editorial_process">peer-reviewed</a> open access journal of <em>s</em>tatistical analysis of ecology.<br />
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<div style="margin-bottom: 15px;"><strong>Impact Factor:</strong> 3 (2018)
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<a class="title-link" href="2018.html">Coupling Traditional Monitoring and Citizen Science to Disentangle the Invasion of Halyomorpha halys</a>
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by Robert Malek, Clara Tattoni, Marco Ciolli, Stefano Corradini, Daniele Andreis, Aya Ibrahim, Valerio Mazzoni, Anna Eriksson, Gianfranco Anfora
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<div class="color-grey-dark"><em>Ecology &amp; Bioinformatics</em> <strong>2018</strong>, <em>1</em>(1), 1; Citation times: 3</div>
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The brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae), is an invasive pest that has expanded its range outside of its original confinements in Eastern Asia, spreading through the United States, Canada and most of the European and Eurasian countries. The invasiveness of this agricultural and public nuisance pest is facilitated by the availability of an array of suitable hosts, an r-selected life history and the release from natural enemies in the invaded zones. Traditional monitoring methods are usually impeded by the lack of time and resources to sufficiently cover large geographical ranges. Therefore, the citizen science initiative “BugMap” was conceived to complement and assist researchers in breaking down the behavior of this invasive pest via a user-friendly, freely available mobile application. The collected data were employed to forecast its predicted distribution and to identify the areas at risk in Trentino, Northern Italy. Moreover, they permitted the uncovering of the seasonal invasion dynamics of this insect, besides providing insight into its phenological patterns, life cycle and potential management methods. Hence, the outcomes of this work emphasize the need to further integrate citizens in scientific endeavors to resolve ecological complications and reduce the gap between the public and science.
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      <a class="title-link" href="2016.html">最大熵(Maxent)模型在物种栖息地研究中的应用</a>
      <div class="authors"> by 周海涛, 那晓东, 臧淑英, 解瑞峰</div>
      <div class="color-grey-dark"><em>Ecology &amp; Bioinformatics</em> <strong>2016</strong>, <em>1</em>(1), 1; Citation times: 23</div>
      <div class="abstract-div"> <a href="#" onclick="if (!window.__cfRLUnblockHandlers) return false; $(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;" > <strong>Abstract </strong> </a>
        <div class="abstract-cropped inline"> 最大熵(Maxent)模型是以生态位理论为基础的新兴研究领域，它通过有限的物种分布点数据及其相关的环境信息组成训练样本，利用基于数据驱动的机器学习理论分析推算出物种的生态需求，然后将运算结果投射至不同的时间和空间中预测物种的潜在分布和实际分布。近年来，该模型在生态学和生物多样性保护等研究中越发重要，文章介绍了Maxent模型的基本原理，从物种栖息地需求分析、气候变化对物种分布的影响、物种入侵监测以及自然保护区的选择和规划设计等方面阐述Maxent模型在物种栖息地研究中的应用。</div>
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      <a class="title-link" href="2015.html">MAXENT最大熵模型在预测物种潜在分布范围方面的应用</a>
      <div class="authors"> by 张路</div>
      <div class="color-grey-dark"><em>Ecology &amp; Bioinformatics</em> <strong>2015</strong>, <em>1</em>(1), 1; Citation times: 10</div>
      <div class="abstract-div"> <a href="#" onclick="if (!window.__cfRLUnblockHandlers) return false; $(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;" > <strong>Abstract </strong></a>
        <div class="abstract-cropped inline"> MAXENT最大熵模型是以最大熵理论为基础的物种地理尺度空间分布模型。该模型自提出之后在国内外迅速得到广泛应用，被越来越多地应用于入侵生物学、保护生物学、全球气候变化对物种分布影响，以及进化生物学等领域的研究。主要从MAXENT模型在入侵生物潜在分布区预测、濒危物种及有经济价值物种的适生区预测，以及全球气候变化对物种分布的影响预测等3个方面，对其应用现状进行综述，分析应用该模型时应该注意的一些问题。</div>
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      <a class="title-link" href="2014.html">基于Maxent的两种入侵性鱼类(麦穗鱼和鲫)的全球适生区预测</a>
      <div class="authors"> by 张熙骜, 隋晓云, 吕植, 陈毅峰</div>
      <div class="color-grey-dark"><em>Ecology &amp; Bioinformatics</em> <strong>2014</strong>, <em>1</em>(1), 1; Citation times: 48</div>
      <div class="abstract-div"> <a href="#" onclick="if (!window.__cfRLUnblockHandlers) return false; $(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;" > <strong>Abstract </strong></a>
        <div class="abstract-cropped inline"> 受引种、贸易等人类活动的影响, 麦穗鱼(Pseudorasbora parva)和鲫(Carassius auratus)在全球范围内已经广泛分布并造成巨大的生态危害。根据野外采样和文献记录, 本文系统整理了麦穗鱼和鲫在世界各地的分布情况, 以高达2.5弧分分辨率的环境数据底图, 利用Maxent模型预测了麦穗鱼和鲫在全球的适生区, 以期为防控麦穗鱼和鲫的入侵提供早期预警。结果表明麦穗鱼和鲫在全球范围的分布区非常广泛, 除南极洲外的各个大洲均有其适生区, 因此这两种鱼还有继续扩散的潜力, 并可能在美国、巴西和阿根廷等国家出现由生物入侵导致的生态学问题。麦穗鱼的适生区主要集中在15º–55º N之间, 欧洲是麦穗鱼入侵的重灾区, 尤其是法国、荷兰周边的西欧国家和匈牙利、塞尔维亚周边的东欧国家; 而美国中部的密西西比河流域、东部及南部沿海, 以及西雅图至加拿大的温哥华之间是麦穗鱼潜在入侵风险性极高的区域。鲫自然分布于欧洲至东亚的广大地区, 目前已经在澳大利亚、加拿大、美国、马达加斯加、印度和越南等国家有分布, 未来还可能进一步扩散至大洋洲的新西兰和新喀里多尼亚, 北美洲的墨西哥至南美洲的阿根廷, 以及非洲的塞内加尔、几内亚和南非等国家; 尤其是南美洲的阿根廷和巴西, 非洲西部的几内亚、喀麦隆等国家将是鲫入侵风险极高的区域。</div>
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      <a class="title-link" href="2013.html">A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter</a>
      <div class="authors"> by Cory Merow, Matthew J. Smith, John A. Silander Jr</div>
      <div class="color-grey-dark"><em>Ecology &amp; Bioinformatics</em> <strong>2013</strong>, <em>1</em>(1), 1; Citation times: 1121</div>
      <div class="abstract-div"> <a href="#" onclick="if (!window.__cfRLUnblockHandlers) return false; $(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;" > <strong>Abstract </strong></a>
        <div class="abstract-cropped inline"> The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt’s calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt’s outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions.</div>
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      <a class="title-link" href="2010.html">A statistical explanation of MaxEnt for ecologists</a>
      <div class="authors"> by Jane Elith, Steven J. Phillips, Trevor Hastie, Miroslav Dudík, Yung En Chee, Colin J. Yates</div>
      <div class="color-grey-dark"><em>Ecology &amp; Bioinformatics</em> <strong>2010</strong>, <em>1</em>(1), 1; Citation times: 2523</div>
      <div class="abstract-div"> <a href="#" onclick="if (!window.__cfRLUnblockHandlers) return false; $(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;" > <strong>Abstract </strong></a>
        <div class="abstract-cropped inline"> MaxEnt is a program for modelling species distributions from presence‐only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence‐only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack of information on species prevalence. The keystone of the paper is a new statistical explanation of MaxEnt which shows that the model minimizes the relative entropy between two probability densities (one estimated from the presence data and one, from the landscape) defined in covariate space. For many users, this viewpoint is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts. We then step through a detailed explanation of MaxEnt describing key components (e.g. covariates and features, and definition of the landscape extent), the mechanics of model fitting (e.g. feature selection, constraints and regularization) and outputs. Using case studies for a Banksia species native to south‐west Australia and a riverine fish, we fit models and interpret them, exploring why certain choices affect the result and what this means. The fish example illustrates use of the model with vector data for linear river segments rather than raster (gridded) data. Appropriate treatments for survey bias, unprojected data, locally restricted species, and predicting to environments outside the range of the training data are demonstrated, and new capabilities discussed. Online appendices include additional details of the model and the mathematical links between previous explanations and this one, example code and data, and further information on the case studies.</div>
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      <a class="title-link" href="2008.html">美国大陆外来入侵物种斑马纹贻贝(Dreissena polymorpha)潜在生境预测模型</a>
      <div class="authors"> by 李明阳, 巨云为, Sunil Kumar, Thomas J. Stohlgren</div>
      <div class="color-grey-dark"><em>Ecology &amp; Bioinformatics</em> <strong>2008</strong>, <em>1</em>(1), 1; Citation times: 131</div>
      <div class="abstract-div"> <a href="#" onclick="if (!window.__cfRLUnblockHandlers) return false; $(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;" > <strong>Abstract </strong></a>
        <div class="abstract-cropped inline"> 防止外来生物入侵造成危害的重要手段是阻止可能造成入侵的物种进入适合其生存的地区.论文以1864个美国外来入侵物种斑马纹贻贝定点发生数据和开放式基础地理信息数据库Daymet的34个环境变量为主要信息源,采用逻辑斯蒂回归(LR),分类与回归树模型(CART),基于规则的遗传算法(GARP),最大熵法(Maxent)4种途径,建立美国大陆部分潜在生境预测模型,从接受者运行特征曲线下面积(AUC),Pearson相关系数,Kappa值3个方面来检验模型预测精度,在此基础上分析斑马纹贻贝的空间分布规律及其环境影响因素.研究结果表明:在3个评价指标中,4个生态位模型预测精度均达到优良水平,其中Maxent在物种现实生境模拟,主要生态环境因子筛选,环境因子对物种生境影响的定量描述方面都表现出了优越的性能;距水源距离,海拔高度,降水频率,太阳辐射是影响物种空间分布的主要环境因子.论文提出的研究方法对中国外来入侵物种生境预测具有较强的借鉴意义,研究结果对中国海洋外来入侵物种沙筛贝的预测与防治,具有一定的指导作用.</div>
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      <a class="title-link" href="2006.html">Maximum entropy modeling of species geographic distributions</a>
      <div class="authors"> by Steven J. Phillips, Robert P. Anderson, Robert E. Schapire</div>
      <div class="color-grey-dark"><em>Ecology &amp; Bioinformatics</em> <strong>2006</strong>, <em>1</em>(1), 1; Citation times: 9738</div>
      <div class="abstract-div"> <a href="#" onclick="if (!window.__cfRLUnblockHandlers) return false; $(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;" > <strong>Abstract </strong></a>
        <div class="abstract-cropped inline"> The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In order to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical mammals: a lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP). We made predictions on 10 random subsets of the occurrence records for both species, and then used the remaining localities for testing. Both algorithms provided reasonable estimates of the species’ range, far superior to the shaded outline maps available in field guides. All models were significantly better than random in both binomial tests of omission and receiver operating characteristic (ROC) analyses. The area under the ROC curve (AUC) was almost always higher for Maxent, indicating better discrimination of suitable versus unsuitable areas for the species. The Maxent modeling approach can be used in its present
form for many appliations with presence-only datasets, and merits further research and development.</div>
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      <a class="title-link" href="2003.html">Evaluating predictive models of species’ distributions: criteria for selecting optimal models</a>
      <div class="authors"> by Steven J. Phillipsa, Robert P. Anderson, Robert E. Schapired</div>
      <div class="color-grey-dark"><em>Ecology &amp; Bioinformatics</em> <strong>2003</strong>, <em>1</em>(1), 1; Citation times: 2277</div>
      <div class="abstract-div"> <a href="#" onclick="if (!window.__cfRLUnblockHandlers) return false; $(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;" > <strong>Abstract </strong></a>
        <div class="abstract-cropped inline"> The Genetic Algorithm for Rule-Set Prediction (GARP) is one of several current approaches to modeling species' distributions using occurrence records and environmental data. Because of stochastic elements in the algorithm and underdetermination of the system (multiple solutions with the same value for the optimization criterion), no unique solution is produced. Furthermore, current implementations of GARP utilize only presence data—rather than both presence and absence, the more general case. Hence, variability among GARP models, which is typical of genetic algorithms, and complications in interpreting results based on asymmetrical (presence-only) input data make model selection critical. Generally, some locality records are randomly selected to build a distributional model, with others set aside to evaluate it. Here, we use intrinsic and extrinsic measures of model performance to determine whether optimal models can be identified based on objective intrinsic criteria, without resorting to an independent test data set. We modeled potential distributions of two rodents ( Heteromys anomalus and Microryzomys minutus) and one passerine bird ( Carpodacus mexicanus), creating 20 models for each species. For each model, we calculated intrinsic and extrinsic measures of omission and commission error, as well as composite indices of overall error. Although intrinsic and extrinsic composite measures of overall model performance were sometimes loosely related to each other, none was consistently associated with expert-judged model quality. In contrast, intrinsic and extrinsic measures were highly correlated for both omission and commission in the two widespread species (H. anomalus and C. mexicanus). Furthermore, a clear inverse relationship existed between omission and commission there, and the best models were consistently found at low levels of omission and moderate-to-high commission values. In contrast, all models for M. minutus showed low values of both omission and commission. Because models are based only on presene data (and not all areas are adequately sampled), the commission index reflects not only true commission error but also a component that results from undersampled areas that the species actually inhabits. We here propose an operational procedure for determining an optimal region of the omission/commission relationship and thus selecting high-quality GARP models. Our implementation of this technique for H. anomalus gave a much more reasonable estimation of the species’ potential distribution than did the original suite of models. These findings are relevant to evaluation of other distributional-modeling techniques based on presence-only data and should also be considered with other machine-learning applications modified for use with asymmetrical input data.</div>
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                    if ($(this).closest("#basic_search").length > 0) {
                        if ($(".search-container__advanced").first().is(":visible")) {
                            openAdvanced()
                        }
                    }

                    if (Foundation.utils.is_small_only()) {
                        if ($(this).hasClass("active")) {
                            $(this).removeClass("active");
                            $(this).next(".custom-accordion-for-small-screen-content").addClass("show-for-medium-up");
                        }
                        else {
                            $(this).addClass("active");
                            $(this).next(".custom-accordion-for-small-screen-content").removeClass("show-for-medium-up");
                            $(document).foundation('orbit', 'reflow');
                        }
                    }

                    if (undefined !== $(this).data("callback")) {
                        var customCallback = $(this).data("callback");
                        func = window[customCallback];
                        func();
                    }
                });

                $(document).on("click", ".js-open-small-search", function(e) {
                    e.preventDefault();  
                    $(this).toggleClass("active").closest(".tab-bar").toggleClass("active");
                    $(".search-container").toggleClass("hide-for-small-down");
                });

                $(document).on("click", ".js-survey-link", function(e) {
                    localStorage.setItem("mdpi_disable_survey", true);
                    window.location.href = "/disable_survey";
                });

                $(document).on("click", ".js-disable-survey-link", function(e) {
                    localStorage.setItem("mdpi_disable_survey", true);
                });

                $(document).on("click", ".js-open-menu", function(e) {
                    $(".search-container").addClass("hide-for-small-down");

                });

                $(window).on('resize', function() {
                    recalculate_main_browser_position();
                    recalculate_responsive_moving_containers();
                });

                checkCookiesAllowed();
                updateSearchLabelVisibilities();
                recalculate_main_browser_position();
                recalculate_responsive_moving_containers();
                
                
                if (window.document.documentMode == 11) {
                    $("<link/>", { rel: "stylesheet", type: "text/css", href: "https://fonts.googleapis.com/icon?family=Material+Icons"}).appendTo("head");
                }
            });

            function recalculate_main_browser_position() {
                if (Foundation.utils.is_small_only()) {
                    if ($("#js-main-top-container").parent("#js-large-main-top-container").length > 0) {
                        $("#js-main-top-container").appendTo($("#js-small-main-top-container"));
                    }
                }
                else {
                    if ($("#js-main-top-container").parent("#js-small-main-top-container").length > 0) {
                        $("#js-main-top-container").appendTo($("#js-large-main-top-container"));
                    }
                }
            }

            function recalculate_responsive_moving_containers() {
                $(".responsive-moving-container.large").each(function() {
                    var previousParent = $(".responsive-moving-container.active[data-id='"+$(this).data("id")+"']");
                    var movingContent  = previousParent.html();

                    if (Foundation.utils.is_small_only()) {
                        var currentParent = $(".responsive-moving-container.small[data-id='"+$(this).data("id")+"']");
                    }
                    else if (Foundation.utils.is_medium_only()) {
                        var currentParent = $(".responsive-moving-container.medium[data-id='"+$(this).data("id")+"']");
                    }
                    else {
                        var currentParent = $(".responsive-moving-container.large[data-id='"+$(this).data("id")+"']");
                    }

                    if (previousParent.attr("class") !== currentParent.attr("class")) {
                        currentParent.html(movingContent);
                        previousParent.html();
                        currentParent.addClass("active");
                        previousParent.removeClass("active");
                    }
                });
            }

            // cookies allowed is checked from a) local storage and b) from server separately so that the footer bar doesn't
            // get included in the custom page caches
            function checkCookiesAllowed() {
                var cookiesEnabled = localStorage.getItem("mdpi_cookies_enabled"); 

                if (null === cookiesEnabled) {
                    $.ajax({
                        url: "/ajax_cookie_value/mdpi_cookies_accepted",
                        success: function(data)
                        {
                            if (data.value) {
                                localStorage.setItem("mdpi_cookies_enabled", true);
                                checkDisplaySurvey();
                            }
                            else {
                                $(".js-allow-cookies").show();
                            }
                        }
                    });
                }
                else {
                    checkDisplaySurvey();
                }
            }

            // cookies allowed is checked from a) local storage and b) from server separately so that the footer bar doesn't
            // get included in the custom page caches
            //
            // NOTE! survey has been closed. Not used anymore (this code can be moved to somewhere for storage in case we need similar logic with other iterations)
            // 
            function checkDisplaySurvey() {
                
                var surveyDisabled = localStorage.getItem("mdpi_disable_survey"); 
                var viewCount      = localStorage.getItem("mdpi_new_layout_views"); 
                var viewLimit      = localStorage.getItem("mdpi_new_layout_limit");

                if (null !== surveyDisabled) {
                    return;
                }

                $('head').append('');

                return;
                // todo: the below part can be removed later on...

                if (null === viewLimit) {
                    localStorage.setItem("mdpi_new_layout_limit", Math.floor(Math.random() * 50));
                    localStorage.setItem("mdpi_new_layout_views", 0);
                }
                else {
                    viewCount++;
                    localStorage.setItem("mdpi_new_layout_views", viewCount);

                    if (viewCount >= viewLimit) {
                        $.ajax({
                            url: "/ajax_cookie_value/mdpi_disable_survey",
                            success: function(data)
                            {
                                if (data.value) {
                                    localStorage.setItem("mdpi_disable_survey", true);
                                }
                                else {
                                    $(".js-survey").show();
                                }
                            }
                        });
                    }
                }
            }
            
        </script>
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<script type="906f863105f9043e3f2b794d-text/javascript">
        $(document).ready(function()
        {
            var helpFunctions = $(".middle-column__help__fixed");
            var middleColumn  = $("#middle-column");

            helpFunctions.affix({
                offset: {
                    top: function() {
                        return middleColumn.offset().top - 8 - (Foundation.utils.is_medium_only() ? 30 : 0);
                    },
                    bottom: function() {
                        return $("#footer").innerHeight() + 56 + (Foundation.utils.is_medium_only() ? 0 : 0);
                    }
                }
            });

            new ClipboardJS('.js-clipboard-copy');
        });
    </script>
<script type="906f863105f9043e3f2b794d-text/javascript">
        $(document).ready(function()
        {
            // create the left hand menu dynamically from the content
            var items = $("#middle-column h1, #middle-column h2");
            if ($("#dynamic-menu").length == 1 && items.length > 1) 
            {
                // menu container div
                var div = $("div#dynamic-menu");
                div.addClass("generic-item");

                // menu header
                var header = $("<h2></h2>");
                header.text("Menu");
                div.append(header);

                // menu list
                var ul = $("<ul></ul>");
                ul.addClass("side-menu-ul");
                div.append(ul);

                // menu list items (create additional anchors for page)
                items.each(function()
                {
                    var header_title = $(this).text();
                    var link_title   = header_title.replace(/ |-/gi, "_").toLowerCase();

                    var li = $("<li></li>");
                    li.addClass("side-menu-li");
                    ul.append(li);

                    var a = $("<a></a>");
                    a.html(header_title);
                    a.prop("href", "#" + link_title);
                    li.append(a);

                    var a = $("<a></a>");
                    a.prop("name", link_title);
                    $(this).prepend(a);
                });

                div.append(ul);
                div.show();
            }
        });
    </script>
<link rel="stylesheet" href="https://www.mdpi.com/assets/css/magnific-popup.min.css?04d343e036f8eecd">
<link rel="stylesheet" href="https://www.mdpi.com/assets/css/jquery-ui-1.10.4.custom.min.css?80647d88647bf347">
<script src="https://www.mdpi.com/assets/js/jquery-ui-1.12.0.min.js?4c2c52cc11e5a28d" type="906f863105f9043e3f2b794d-text/javascript"></script>
<script type="906f863105f9043e3f2b794d-text/javascript" src="https://www.mdpi.com/assets/js/magnific-popup.min.js?2be3d9e7dc569146"></script>
<script type="906f863105f9043e3f2b794d-text/javascript">
        var mainColumn1    = "#right-column";
        var extendingReady = true;

        $(document).ready(function()
        {
            $("#journal-browser-go").toggleClass("button--grey",  "" === $("#journal-browser-volume").val());
            $("#journal-browser-go").toggleClass("button--color", "" !== $("#journal-browser-volume").val());

            $("#journal-browser-volume").change(function(e) {
                $('#journal-browser-issue').find('option').not('.volume-0').hide(); 
                $('#journal-browser-issue').find('.volume-' + $(this).val()).show(); 
                $('#journal-browser-issue').find('option:first').prop('selected', 'selected'); 
                $("#journal-browser-issue").trigger("chosen:updated");

                $('#journal-browser-go').toggleClass('button--grey',  '' === $(this).val());  
                $('#journal-browser-go').toggleClass('button--color', '' !== $(this).val());  
            });

            // add resize event for the window (to recalculate side column elements)
            // TODO: is it better to use resize end or resize here?
            $(window).on('resize', function() {
                mdpi_column_height_module.calculateColumnHeights(false, mainColumn1);
            });

            $(".link-journal-menu").click(function(e) {
                e.preventDefault();

                $(this).find('span').toggle();
                $(this).next("ul").toggleClass("active");
                $("#social-media-links").toggle();
                $("#journal-alerts").toggle();
            });

            $(".link-journal-browser").click(function(e) {
                e.preventDefault();

                $(this).find('span').toggle();
                $(this).next("div").toggleClass('show-for-medium-up');
            });

                    });
    </script>
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